{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任务一：\n",
    "输入1（cycle）2（EMG_retus_femoris_l=emg_rf_l），输出4（muscleForce_retus_femoris_l=mf_rf_l）\n",
    "\n",
    "似乎把2做整体输入时，cycle可以省略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
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      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "# 可视化\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "#path = 'G:\\科研学习\\肌电信号\\Code\\musle force\\\\'\n",
    "path ='/mnt/data/ZP_Project/Muscle/muscle/'\n",
    "Visdom_Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Visdom\\\\'\n",
    "if not os.path.exists(Visdom_Dir):\n",
    "    os.makedirs(Visdom_Dir)\n",
    "image_Dir =path+'image_Mission1_FC_all_together'\n",
    "if not os.path.exists(image_Dir):\n",
    "        os.makedirs(image_Dir)\n",
    "# image_Dir ='G:\\科研学习\\肌电信号\\Code\\musle force\\image_Mission3_FC\\\\'\n",
    "# if not os.path.exists(image_Dir):\n",
    "#         os.makedirs(image_Dir)\n",
    "\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "Dir = 'Transed_Data'\n",
    "filenames = os.listdir(path+Dir)\n",
    "# Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\\\'\n",
    "# filenames = os.listdir('G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data')\n",
    "\n",
    "data_emg = []\n",
    "data_mf = []\n",
    "#data_kal = []\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = scio.loadmat(path+Dir +'//'+ str(filename))\n",
    "    #print(data.keys())  #dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n",
    "    data1 = data['emg_rf_l']\n",
    "    #data1 = data1.squeeze()\n",
    "    data2 = data['mf_rf_l']\n",
    "    #data3 =  data['ka_l']\n",
    "    #data2 = data2.squeeze()\n",
    "    data_emg.append(data1)\n",
    "    data_mf.append(data2)\n",
    "    #data_kal.append(data3)\n",
    "\n",
    "data_emg = np.array(data_emg)\n",
    "data_mf = np.array(data_mf)\n",
    "#data_kal = np.array(data_kal)\n",
    "set=MuscleData1(data_emg,data_mf)\n",
    "train_set,test_set = torch.utils.data.random_split(set, [28, 4])\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "\n",
    "Net = FC_Net()\n",
    "Net.apply(weights_init)\n",
    "Net.to(device)\n",
    "\n",
    "train_loader=torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False)\n",
    "test_loader=torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False)\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 10000\n",
    "\n",
    "optimizer = torch.optim.SGD(Net.parameters(),\n",
    "lr=0.0000015,momentum=0.9)\n",
    "vizx = 0\n",
    "#开始训练\n",
    "for epoch in range(epoch_num):\n",
    "        #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "        for batch in train_loader:\n",
    "                vizx+=1\n",
    "                data,label = batch\n",
    "                data = torch.as_tensor(data)\n",
    "                #data2 = torch.as_tensor(data2)\n",
    "                data = data.type(torch.FloatTensor)\n",
    "                #data2 = data2.type(torch.FloatTensor)\n",
    "                \n",
    "                #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                label =torch.as_tensor(label)\n",
    "                label = label.squeeze(2)\n",
    "                #print(label.shape)\n",
    "                label = label.type(torch.FloatTensor)\n",
    "                data = data.cuda()\n",
    "                #data2 = data2.cuda()\n",
    "                label = label.cuda()\n",
    "                #print(data.shape)\n",
    "                pred = Net(data)\n",
    "                #print(pred.shape)\n",
    "                #print(label.shape)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                #print('pred',pred.shape)\n",
    "                #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                loss = criterion(pred,label)\n",
    "                \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                viz_batch[0].line([float(loss)],[vizx],win='loss'+str(0), name='loss'+str(0), update='append',opts=dict(title='train_loss'))\n",
    "                optimizer.step()\n",
    "                #n +=1\n",
    "                #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i =0\n",
    "#测试\n",
    "for batch in test_loader:\n",
    "        data1,label = batch\n",
    "        data1 = torch.as_tensor(data1)\n",
    "        #data2 = torch.as_tensor(data2)\n",
    "        data1 = data1.type(torch.FloatTensor)\n",
    "        #data2 = data2.type(torch.FloatTensor)\n",
    "        data1 = data1.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        #data2 = data2.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        # label =label.to(device)\n",
    "        #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "        pred = Net(data1)\n",
    "        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "        pred = pred.cpu()\n",
    "        pred = pred.data.numpy()\n",
    "        pred=np.squeeze(pred)\n",
    "        label=np.squeeze(label)\n",
    "        data1 =data1.cpu()\n",
    "        #data2 =data2.cpu()\n",
    "        data1 = np.squeeze(data1)\n",
    "        #data2 = np.squeeze(data2)\n",
    "        #利用plot画图\n",
    "        plt.figure(i)\n",
    "        plt.title('test_sample'+str(i))\n",
    "        plt.plot(pred, label='pred')\n",
    "        plt.plot(label, color='red', label='GroundTruth')\n",
    "        plt.plot(data1*100, color='black', label='data1')\n",
    "        #plt.plot(data2*100, color='black', label='data2')\n",
    "        plt.legend()\n",
    "        #增\n",
    "        figname ='./image_Mission1_FC_all_together/imag_ {}.jpg'.format(i)\n",
    "        plt.savefig(figname)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "        i +=1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #用于打印网络里权重。默认注释掉\n",
    "# Net.state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3b95d8d6e207246d09e19daee9bbc5bbe4502ff924268508716ea3969933742"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
